What is prompt injection and why is it dangerous?

Prompt injection is an attack technique where malicious input manipulates an AI system to perform unintended actions — such as leaking confidential data, bypassing security rules, or executing commands. It ranks #1 in the OWASP LLM Top 10 because nearly all Large Language Models are vulnerable to it without additional security measures.

Is my AI system vulnerable if I use a commercial LLM?

Yes, even with commercial LLMs like GPT or Claude you are vulnerable to prompt injection, data leakage, and insecure output handling. The vulnerabilities exist not only in the model itself, but also in how you integrate it into your application, what data you provide to it, and how you process the output.

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AI Beveiliging Nederland

Wij bieden AI Beveiliging in Nederland aan MKB+ organisaties.

AI-beveiligingsrisico's zijn fundamenteel anders dan klassieke applicatiekwetsbaarheden. Prompt injection, datalekken via model-output en training-data-vergiftiging staan bovenaan de OWASP LLM Top 10 — en geen firewall kan ze automatisch stoppen. Onze AI-security assessments combineren automatische scanning met handmatige red teaming gebaseerd op huidige aanvalstechnieken. U krijgt een concreet actieplan, geen lijvig rapport dat stof verzamelt.

OWASP LLM Top 10 LLM Pentesting AI Red Teaming Prompt Injection ISO 42001

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LLM Security

LLM Pentesting — a new attack surface

An LLM processing production data, answering customer questions, or controlling internal processes is an attractive target. Our assessments follow the OWASP LLM Top 10 framework.

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Prompt Injection Testing

The most critical LLM vulnerability. Direct injection via the user interface, indirect injection via external data sources that the model reads (websites, documents, databases).

Security filter bypass testing
System prompt extraction attempts
Scope boundary violation testing

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Data Leakage & Jailbreaking

LLMs with access to sensitive data can disclose it via targeted prompts. We test for training data extraction, jailbreaks, system prompt leakage, and RAG data leakage.

PII & trade secret extraction
RAG unauthorized document retrieval
Security restriction bypass

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Model Poisoning & Evasion

Integrity attacks on training pipelines — poisoned data, backdoors, fine-tuning manipulation. We stress-test ML training pipelines and inference results.

Training data integrity checks
Backdoor detection
Adversarial robustness testing

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AI Red Team Approach

Structured red team exercise: model inventory, threat modeling, exploitation attempts, findings report with risk scores and concrete remediation steps.

OWASP LLM Top 10 coverage
Risk-scored findings report
Remediation guidance included

OWASP LLM Top 10

Complete coverage of the OWASP LLM Top 10 framework

Our assessments cover all ten vulnerability categories from the OWASP LLM Top 10 framework.

LLM01

Prompt Injection

LLM02

Insecure Output Handling

LLM03

Training Data Poisoning

LLM04

Model Denial of Service

LLM05

Supply Chain Vulns

LLM06

Sensitive Info Disclosure

LLM07

Insecure Plugin Design

LLM08

Excessive Agency

LLM09

Overreliance on AI

LLM10

Model Theft & Extraction

AI Governance & ISO 42001

Why AI Governance is now essential

AI-systemen nemen steeds vaker beslissingen die mensen raken. Without governance, organizations risk discriminatory outcomes, reputational damage, legal liability, and non-compliance with the EU AI Act.

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ISO 42001:2023 — AI Management System

ISO/IEC 42001:2023 is the international standard for establishing and managing an AI Management System (AIMS). It provides a structured framework for responsible AI use, governance, and risk management. Integrates directly with ISO 27001.

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EU AI Act Compliance

The EU AI Act categorizes AI systems by risk level. High-risk systems require conformity assessments, human oversight, and robust documentation. We implement the required measures.

AI Governance services

AI Management System (AIMS) setup
AI risk and impact assessments
EU AI Act gap analysis and compliance
AI policy development
AI transparency & explainability
NIST AI RMF implementation
AI Ethics framework
ISO 42001 certification pathway

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